import numpy as np

# data1 = np.genfromtxt('F:\AIOPS\基于迁移学习的多指标异常检测\code\label-tool-cyp\data\移动_txt_400_raw\data_0_99.txt', delimiter=',')
# data2 = np.genfromtxt('F:\AIOPS\基于迁移学习的多指标异常检测\code\label-tool-cyp\data\移动_txt_400_raw\data_100_199.txt', delimiter=',')
# data3 = np.genfromtxt('F:\AIOPS\基于迁移学习的多指标异常检测\code\label-tool-cyp\data\移动_txt_400_raw\data_200_299.txt', delimiter=',')
# data4 = np.genfromtxt('F:\AIOPS\基于迁移学习的多指标异常检测\code\label-tool-cyp\data\移动_txt_400_raw\data_300_399.txt', delimiter=',')
data_all = np.genfromtxt('F:\AIOPS\移动研究院\code\label-tool-cyp\data\移动_txt_400_raw\data_all.txt', delimiter=',')
a_data_num = 96 * 14

# data = np.vstack([data1[:50*a_data_num], data3[:50*a_data_num],data1[50*a_data_num:], data2, data3[50*a_data_num:], data4])


all_data_list = []
for data_index in range(200):
    data_item = data_all[data_index*a_data_num:(data_index+1)*a_data_num]
    # data_item = np.transpose(data_item)
    # all_data_list.append(data_item)
    np.savetxt(f'F:\AIOPS\移动研究院\code\label-tool-cyp\data\移动_txt_200\{data_index}.txt', data_item, delimiter=',', fmt='%.2f')

# np.save(r'F:\AIOPS\基于迁移学习的多指标异常检测\code\label-tool-cyp\preprocess_data\data\yidong_data.npy', np.array(all_data_list))